[slimsag]

For the author: there's a small typo "Discrimniator" instead of "Discriminator" in the video at 1:11

One thing I was confused by: the video says the discriminator "AI" is trained to detect true vs. generated results, with the hope the generator becomes good enough to fool the discriminator. But why is the discriminator useful, then? Couldn't you just tell generator "AI" whether the result it produced was true or not?

I think the answer is.. you don't want just a perfect recreation of the training data you gave to the generator, instead you want the generator to produce variations of that training data, so there's a "how would you know if it's 'a true result' / good enough?" problem. So the discriminator is useful because it's not a direct comparison, but rather a "this looks approximately good enough" comparison of the true vs. generated result.

This all makes me wonder: what sort of data set needs to be fed to the discriminator to train it? Is it some sort of "true image" and "true image w/bad alterations (e.g. lines, scratches, etc.) to it" data set?

[FeepingCreature]

If you think about how humans draw things, there's a repeated process of "create content" to "inspect for issues" back to "create corrections", "inspect for issues" etc. It seems that generation and criticism are two rather distinct skills, which is why GANs, generative-adversarial networks that contain both a "generator" and a "critic" network, end up making fewer mistakes and learning distant correlations better.

[liuru]

Thank you for the typo!

Indeed, it contributes to the variations problem.

also: If the discriminator starts off perfect, then the generator can't learn to be better.

Sort of like a human learning to play chess: If you start off with top-tier opponents that crush you, then you don't have a gradient to learn from. Instead, you need players at your own level to grow your skills.